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| import argparse import os import sys
import numpy as np from PIL import Image
import chainer from chainer import cuda import chainer.functions as F import chainer.links from chainer.links import caffe from chainer import Variable, optimizers
import pickle
def subtract_mean(x0): x = x0.copy() x[0,0,:,:] -= 120 x[0,1,:,:] -= 120 x[0,2,:,:] -= 120 return x def add_mean(x0): x = x0.copy() x[0,0,:,:] += 120 x[0,1,:,:] += 120 x[0,2,:,:] += 120 return x
def image_resize(img_file, width): gogh = Image.open(img_file) orig_w, orig_h = gogh.size[0], gogh.size[1] if orig_w>orig_h: new_w = width new_h = width*orig_h//orig_w gogh = np.asarray(gogh.resize((new_w,new_h)))[:,:,:3].transpose(2, 0, 1)[::-1].astype(np.float32) gogh = gogh.reshape((1,3,new_h,new_w)) print("image resized to: ", gogh.shape) hoge= np.zeros((1,3,width,width), dtype=np.float32) hoge[0,:,width-new_h:,:] = gogh[0,:,:,:] gogh = subtract_mean(hoge) else: new_w = width*orig_w//orig_h new_h = width gogh = np.asarray(gogh.resize((new_w,new_h)))[:,:,:3].transpose(2, 0, 1)[::-1].astype(np.float32) gogh = gogh.reshape((1,3,new_h,new_w)) print("image resized to: ", gogh.shape) hoge= np.zeros((1,3,width,width), dtype=np.float32) hoge[0,:,:,width-new_w:] = gogh[0,:,:,:] gogh = subtract_mean(hoge) return xp.asarray(gogh), new_w, new_h
def save_image(img, width, new_w, new_h, it): def to_img(x): im = np.zeros((new_h,new_w,3)) im[:,:,0] = x[2,:,:] im[:,:,1] = x[1,:,:] im[:,:,2] = x[0,:,:] def clip(a): return 0 if a<0 else (255 if a>255 else a) im = np.vectorize(clip)(im).astype(np.uint8) Image.fromarray(im).save(args['out_dir']+"/im_%05d.png"%it)
if args['gpu']>=0: img_cpu = add_mean(img.get()) else: img_cpu = add_mean(img) if width==new_w: to_img(img_cpu[0,:,width-new_h:,:]) else: to_img(img_cpu[0,:,:,width-new_w:])
def get_matrix(y): ch = y.data.shape[1] wd = y.data.shape[2] gogh_y = F.reshape(y, (ch,wd**2)) gogh_matrix = F.matmul(gogh_y, gogh_y, transb=True)/np.float32(ch*wd**2) return gogh_matrix
class Clip(chainer.Function): def forward(self, x): x = x[0] ret = cuda.elementwise( 'T x','T ret', ''' ret = x<-120?-120:(x>136?136:x); ''','clip')(x) return ret
def generate_image(img_orig, img_style, width, nw, nh, max_iter, lr, img_gen=None): mid_orig = nn.forward(Variable(img_orig)) style_mats = [get_matrix(y) for y in nn.forward(Variable(img_style))]
if img_gen is None: if args['gpu'] >= 0: img_gen = xp.random.uniform(-20,20,(1,3,width,width),dtype=np.float32) else: img_gen = np.random.uniform(-20,20,(1,3,width,width)).astype(np.float32) img_gen = chainer.links.Parameter(img_gen) optimizer = optimizers.Adam(alpha=lr) optimizer.setup(img_gen) for i in range(max_iter): img_gen.zerograds()
x = img_gen.W y = nn.forward(x)
L = Variable(xp.zeros((), dtype=np.float32)) for l in range(len(y)): ch = y[l].data.shape[1] wd = y[l].data.shape[2] gogh_y = F.reshape(y[l], (ch,wd**2)) gogh_matrix = F.matmul(gogh_y, gogh_y, transb=True)/np.float32(ch*wd**2)
L1 = np.float32(args['lam']) * np.float32(nn.alpha[l])*F.mean_squared_error(y[l], Variable(mid_orig[l].data)) L2 = np.float32(nn.beta[l])*F.mean_squared_error(gogh_matrix, Variable(style_mats[l].data))/np.float32(len(y)) L += L1+L2
if i%100==0: print(i,l,L1.data,L2.data)
L.backward() img_gen.W.grad = x.grad optimizer.update()
tmp_shape = x.data.shape if args['gpu'] >= 0: img_gen.W.data += Clip().forward(img_gen.W.data).reshape(tmp_shape) - img_gen.W.data else: def clip(x): return -120 if x<-120 else (136 if x>136 else x) img_gen.W.data += np.vectorize(clip)(img_gen.W.data).reshape(tmp_shape) - img_gen.W.data
if i%50==0: save_image(img_gen.W.data, W, nw, nh, i)
args = {} args['orig_img'] = 'cat.png' args['style_img'] = 'style_6.png' args['out_dir'] = 'result' args['model'] = 'nin_imagenet.caffemodel' args['width'] = 435 args['iter'] = 5000 args['gpu'] = -1 args['lam'] = 0.005 args['lr'] = 4.0
if args['gpu'] >= 0: cuda.check_cuda_available() chainer.Function.type_check_enable = False cuda.get_device(args['gpu']).use() xp = cuda.cupy else: xp = np
if 'nin' in args['model']: nn = NIN() elif 'vgg' == args['model']: nn = VGG() elif 'vgg_chainer' == args['model']: nn = VGG_chainer() elif 'i2v' in args['model']: nn = I2V() elif 'googlenet' in args['model']: nn = GoogLeNet() else: print ('invalid model name. you can use (nin, vgg, vgg_chainer, i2v, googlenet)') if args['gpu']>=0: nn.model.to_gpu()
W = args['width'] img_content,nw,nh = image_resize(args['orig_img'], W) img_style,_,_ = image_resize(args['style_img'], W)
generate_image(img_content, img_style, W, nw, nh, img_gen=None, max_iter=args['iter'], lr=args['lr'])
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